Many researchers are dedicated to developing powerful classifiers for change detection. Li et al. [1]} designed two-level clustering algorithm for unsupervised change detection. In [2]}, local-neighborhood information is embedded in the clustering objective function to improve the change detection performance. Gong et al. [3]} developed an improved Markov random field (MRF) based on fuzzy \(c\) -means (FCM) clustering to suppress the speckle noise. In [4]}, the stacked restricted Boltzmann machines (RBMs) was employed for SAR image change detection. Although the above methods achieved promising performance, the feature representation capabilities are still limited.
